MPLSandbox自动识别并安全执行多语言代码，提升LLM生成代码的准确性和开发效率。
Multi-Programming Language Sandbox for LLMs
- 自动识别代码语言，在隔离环境中编译执行以保障安全
- 集成传统与LLM驱动的分析工具，提供全面代码反馈
- 可无缝接入LLM训练部署，适合代码生成相关研究者
我们提出MPLSandbox，一个开箱即用的多编程语言沙盒，旨在为大型语言模型（LLMs）提供统一且全面的编译器与分析工具反馈。它能自动识别代码语言，并在隔离子沙盒中编译和执行代码，确保安全与稳定。MPLSandbox还集成了传统与基于LLM的代码分析工具，对生成代码进行综合评估。该系统可轻松融入LLM的训练与部署流程，提升生成代码的质量与正确性，帮助研究人员简化各类基于LLM的代码任务工作流，降低开发成本。为验证其有效性，我们将MPLSandbox应用于训练、部署及多种真实场景的代码任务优化中。目标是通过自动化与委托化，显著提升研究者在基于LLM的代码任务上的生产力。
原文摘要 · Abstract (English)
We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox also integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. MPLSandbox can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of their generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we integrate it into training and deployment approaches, and also employ it to optimize workflows for a wide range of real-world code-related tasks. Our goal is to enhance researcher productivity on LLM-based code-related tasks by simplifying and automating workflows through delegation to MPLSandbox.
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